{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用ARMA工具对沪市指数进行预测：\n",
    "\n",
    "Step1，数据加载&探索\n",
    "按照不同的时间尺度（天，月，季度，年）可以将数据压缩，得到不同尺度的数据，然后做可视化呈现。这4个时间尺度上，我们选择“月”作为预测模型的时间尺度\n",
    "df_month = df.resample('M').mean()\n",
    "\n",
    "Step2，模型选择&训练，在给定范围内，选择最优的超参数\n",
    "创建ARMA时间序列模型。我们并不知道p和q取什么值时，模型最优，因此可以给它们设置一个区间范围，比如都是range(0,3)，然后计算不同模型的AIC数值，选择最小的AIC数值对应的那个ARMA模型\n",
    "\n",
    "Step3，模型预测，可视化呈现\n",
    "用这个最优的ARMA模型预测未来3个月的沪市指数走势，并将结果做可视化呈现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 沪市指数走势预测，使用时间序列ARMA\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "import warnings\n",
    "from itertools import product\n",
    "from datetime import datetime, timedelta\n",
    "import calendar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step1，数据加载&探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Price</th>\n",
       "      <th>stock_volume</th>\n",
       "      <th>amount_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990/12/19</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>1260</td>\n",
       "      <td>494000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990/12/20</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>197</td>\n",
       "      <td>84000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990/12/21</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>28</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990/12/24</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>32</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990/12/25</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>15</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7140</th>\n",
       "      <td>2020/3/6</td>\n",
       "      <td>3034.5113</td>\n",
       "      <td>362061533</td>\n",
       "      <td>3.77E+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7141</th>\n",
       "      <td>2020/3/9</td>\n",
       "      <td>2943.2907</td>\n",
       "      <td>414560736</td>\n",
       "      <td>4.38E+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7142</th>\n",
       "      <td>2020/3/10</td>\n",
       "      <td>2996.7618</td>\n",
       "      <td>393296648</td>\n",
       "      <td>4.25E+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7143</th>\n",
       "      <td>2020/3/11</td>\n",
       "      <td>2968.5174</td>\n",
       "      <td>352470970</td>\n",
       "      <td>3.79E+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7144</th>\n",
       "      <td>2020/3/12</td>\n",
       "      <td>2923.4856</td>\n",
       "      <td>307778457</td>\n",
       "      <td>3.28E+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7145 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp      Price  stock_volume amount_volume\n",
       "0     1990/12/19    99.9800          1260        494000\n",
       "1     1990/12/20   104.3900           197         84000\n",
       "2     1990/12/21   109.1300            28         16000\n",
       "3     1990/12/24   114.5500            32         31000\n",
       "4     1990/12/25   120.2500            15          6000\n",
       "...          ...        ...           ...           ...\n",
       "7140    2020/3/6  3034.5113     362061533      3.77E+11\n",
       "7141    2020/3/9  2943.2907     414560736      4.38E+11\n",
       "7142   2020/3/10  2996.7618     393296648      4.25E+11\n",
       "7143   2020/3/11  2968.5174     352470970      3.79E+11\n",
       "7144   2020/3/12  2923.4856     307778457      3.28E+11\n",
       "\n",
       "[7145 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('shanghai_index_1990_12_19_to_2020_03_12.csv')\n",
    "df#沪市指数预测 其中有 时间 价格 股票存量和数量存量   #我们需要的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Price</th>\n",
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       "      <th>0</th>\n",
       "      <td>1990/12/19</td>\n",
       "      <td>99.9800</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990/12/20</td>\n",
       "      <td>104.3900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990/12/21</td>\n",
       "      <td>109.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990/12/24</td>\n",
       "      <td>114.5500</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990/12/25</td>\n",
       "      <td>120.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7141</th>\n",
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       "      <td>2943.2907</td>\n",
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       "      <td>2996.7618</td>\n",
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       "      <th>7143</th>\n",
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       "      <td>2968.5174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7144</th>\n",
       "      <td>2020/3/12</td>\n",
       "      <td>2923.4856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7145 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp      Price\n",
       "0     1990/12/19    99.9800\n",
       "1     1990/12/20   104.3900\n",
       "2     1990/12/21   109.1300\n",
       "3     1990/12/24   114.5500\n",
       "4     1990/12/25   120.2500\n",
       "...          ...        ...\n",
       "7140    2020/3/6  3034.5113\n",
       "7141    2020/3/9  2943.2907\n",
       "7142   2020/3/10  2996.7618\n",
       "7143   2020/3/11  2968.5174\n",
       "7144   2020/3/12  2923.4856\n",
       "\n",
       "[7145 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#实际上只要['Timestamp', 'Price']这两列就\n",
    "df = df[['Timestamp', 'Price']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stu_15527388015/.local/lib/python3.7/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
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       "      <td>1990-12-19</td>\n",
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       "      <td>104.3900</td>\n",
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       "      <th>2</th>\n",
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       "      <td>109.1300</td>\n",
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       "      <th>3</th>\n",
       "      <td>1990-12-24</td>\n",
       "      <td>114.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>120.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>7140</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>3034.5113</td>\n",
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       "    <tr>\n",
       "      <th>7141</th>\n",
       "      <td>2020-03-09</td>\n",
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       "      <th>7142</th>\n",
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       "      <td>2020-03-11</td>\n",
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       "      <th>7144</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2923.4856</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>7145 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Timestamp      Price\n",
       "0    1990-12-19    99.9800\n",
       "1    1990-12-20   104.3900\n",
       "2    1990-12-21   109.1300\n",
       "3    1990-12-24   114.5500\n",
       "4    1990-12-25   120.2500\n",
       "...         ...        ...\n",
       "7140 2020-03-06  3034.5113\n",
       "7141 2020-03-09  2943.2907\n",
       "7142 2020-03-10  2996.7618\n",
       "7143 2020-03-11  2968.5174\n",
       "7144 2020-03-12  2923.4856\n",
       "\n",
       "[7145 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Timestamp的时间格式转换一下  然后当成索引\n",
    "df.Timestamp = pd.to_datetime(df.Timestamp)\n",
    "df#格式已经转换成功了  然后准备当成索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>99.9800</td>\n",
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       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
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       "      <th>1990-12-21</th>\n",
       "      <td>109.1300</td>\n",
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       "      <th>1990-12-24</th>\n",
       "      <td>114.5500</td>\n",
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       "      <th>1990-12-25</th>\n",
       "      <td>120.2500</td>\n",
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       "      <th>...</th>\n",
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       "      <th>2020-03-06</th>\n",
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       "      <th>2020-03-09</th>\n",
       "      <td>2943.2907</td>\n",
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       "      <th>2020-03-10</th>\n",
       "      <td>2996.7618</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>2968.5174</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>2923.4856</td>\n",
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       "<p>7145 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                Price\n",
       "Timestamp            \n",
       "1990-12-19    99.9800\n",
       "1990-12-20   104.3900\n",
       "1990-12-21   109.1300\n",
       "1990-12-24   114.5500\n",
       "1990-12-25   120.2500\n",
       "...               ...\n",
       "2020-03-06  3034.5113\n",
       "2020-03-09  2943.2907\n",
       "2020-03-10  2996.7618\n",
       "2020-03-11  2968.5174\n",
       "2020-03-12  2923.4856\n",
       "\n",
       "[7145 rows x 1 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Timestamp',inplace=True)#或者可以用这句代码df.index = df.Timestamp\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
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       "      <th>1990-12-31</th>\n",
       "      <td>116.990000</td>\n",
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       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>132.628182</td>\n",
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       "      <th>1991-02-28</th>\n",
       "      <td>131.887778</td>\n",
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       "      <th>1991-03-31</th>\n",
       "      <td>126.011429</td>\n",
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       "      <th>1991-04-30</th>\n",
       "      <td>118.426818</td>\n",
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       "      <td>2923.774700</td>\n",
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       "      <th>2019-12-31</th>\n",
       "      <td>2962.063709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>3078.654681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>2927.513035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2990.415289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>352 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "1990-12-31   116.990000\n",
       "1991-01-31   132.628182\n",
       "1991-02-28   131.887778\n",
       "1991-03-31   126.011429\n",
       "1991-04-30   118.426818\n",
       "...                 ...\n",
       "2019-11-30  2923.774700\n",
       "2019-12-31  2962.063709\n",
       "2020-01-31  3078.654681\n",
       "2020-02-29  2927.513035\n",
       "2020-03-31  2990.415289\n",
       "\n",
       "[352 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照月，季度，年来统计\n",
    "df_month = df.resample('M').mean()\n",
    "print(type(df_month))\n",
    "df_month#跟df相比  数据少了很多 因为是按照一个月统计一次  按照每个月的平均值来统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>116.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>130.131803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-06-30</th>\n",
       "      <td>117.945625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-09-30</th>\n",
       "      <td>160.548030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-12-31</th>\n",
       "      <td>240.092656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>2792.941622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>3010.354852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>2923.130869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>2946.748434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2993.832738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "1990-12-31   116.990000\n",
       "1991-03-31   130.131803\n",
       "1991-06-30   117.945625\n",
       "1991-09-30   160.548030\n",
       "1991-12-31   240.092656\n",
       "...                 ...\n",
       "2019-03-31  2792.941622\n",
       "2019-06-30  3010.354852\n",
       "2019-09-30  2923.130869\n",
       "2019-12-31  2946.748434\n",
       "2020-03-31  2993.832738\n",
       "\n",
       "[118 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q = df.resample('Q-DEC').mean()\n",
    "df_Q#这个是按照季度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>116.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-12-31</th>\n",
       "      <td>162.543765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>668.515608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-31</th>\n",
       "      <td>1013.247004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-12-31</th>\n",
       "      <td>674.123214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>657.789203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-12-31</th>\n",
       "      <td>764.639478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-31</th>\n",
       "      <td>1175.584881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-12-31</th>\n",
       "      <td>1261.037159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>1377.330674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>1881.996556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>1956.159812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>1567.231135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>1467.738058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>1482.854667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>1153.550678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>1629.934141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>4237.692008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>3031.825398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>2765.430332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>2830.994508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>2666.891836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>2219.135486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>2191.699298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>2238.214951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>3721.552045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>3003.683787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>3249.687147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>2943.151610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>2919.537157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>2993.832738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "1990-12-31   116.990000\n",
       "1991-12-31   162.543765\n",
       "1992-12-31   668.515608\n",
       "1993-12-31  1013.247004\n",
       "1994-12-31   674.123214\n",
       "1995-12-31   657.789203\n",
       "1996-12-31   764.639478\n",
       "1997-12-31  1175.584881\n",
       "1998-12-31  1261.037159\n",
       "1999-12-31  1377.330674\n",
       "2000-12-31  1881.996556\n",
       "2001-12-31  1956.159812\n",
       "2002-12-31  1567.231135\n",
       "2003-12-31  1467.738058\n",
       "2004-12-31  1482.854667\n",
       "2005-12-31  1153.550678\n",
       "2006-12-31  1629.934141\n",
       "2007-12-31  4237.692008\n",
       "2008-12-31  3031.825398\n",
       "2009-12-31  2765.430332\n",
       "2010-12-31  2830.994508\n",
       "2011-12-31  2666.891836\n",
       "2012-12-31  2219.135486\n",
       "2013-12-31  2191.699298\n",
       "2014-12-31  2238.214951\n",
       "2015-12-31  3721.552045\n",
       "2016-12-31  3003.683787\n",
       "2017-12-31  3249.687147\n",
       "2018-12-31  2943.151610\n",
       "2019-12-31  2919.537157\n",
       "2020-12-31  2993.832738"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_year = df.resample('A-DEC').mean()\n",
    "df_year# 这个是按照年份 一年统计一次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照天，月，季度，年来显示沪市指数的走势\n",
    "fig = plt.figure(figsize=[15, 7])\n",
    "plt.suptitle('Shanghai Stock Exchange Index', fontsize=20)\n",
    "plt.subplot(221)\n",
    "plt.plot(df.Price, '-', label='byDay')\n",
    "plt.legend()\n",
    "plt.subplot(222)\n",
    "plt.plot(df_month.Price, '-', label='byMonth')\n",
    "plt.legend()\n",
    "plt.subplot(223)\n",
    "plt.plot(df_Q.Price, '-', label='bySeason')\n",
    "plt.legend()\n",
    "plt.subplot(224)\n",
    "plt.plot(df_year.Price, '-', label='byYear')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step2，模型选择&训练，在给定范围内，选择最优的超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ps range(0, 3) <class 'range'>\n",
      "qs range(0, 3) <class 'range'>\n",
      "parameters <itertools.product object at 0x7f6065eb6be0> <class 'itertools.product'>\n",
      "parameters_list [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)] <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "# 设置参数范围\n",
    "ps = range(0, 3)\n",
    "print('ps',ps,type(ps))\n",
    "qs = range(0, 3)\n",
    "print('qs',qs,type(qs))\n",
    "parameters = product(ps, qs)\n",
    "print('parameters',parameters,type(parameters))\n",
    "parameters_list = list(parameters)\n",
    "print('parameters_list',parameters_list,type(parameters_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数错误: (0, 2)\n",
      "最优模型:                                ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                  Price   No. Observations:                  352\n",
      "Model:                     ARMA(2, 2)   Log Likelihood               -2290.943\n",
      "Method:                       css-mle   S.D. of innovations            161.347\n",
      "Date:                Tue, 09 Mar 2021   AIC                           4593.887\n",
      "Time:                        08:54:05   BIC                           4617.069\n",
      "Sample:                    12-31-1990   HQIC                          4603.112\n",
      "                         - 03-31-2020                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const        1888.9097    497.478      3.797      0.000     913.872    2863.948\n",
      "ar.L1.Price     0.4820      0.129      3.726      0.000       0.228       0.736\n",
      "ar.L2.Price     0.4832      0.128      3.780      0.000       0.233       0.734\n",
      "ma.L1.Price     0.8712      0.123      7.079      0.000       0.630       1.112\n",
      "ma.L2.Price     0.3708      0.062      6.006      0.000       0.250       0.492\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.0239           +0.0000j            1.0239            0.0000\n",
      "AR.2           -2.0214           +0.0000j            2.0214            0.5000\n",
      "MA.1           -1.1748           -1.1475j            1.6422           -0.3769\n",
      "MA.2           -1.1748           +1.1475j            1.6422            0.3769\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 寻找最优ARMA模型参数，即best_aic最小\n",
    "results = []\n",
    "best_aic = float(\"inf\") # 正无穷\n",
    "for param in parameters_list:#去遍历parameters_list里面的值 \n",
    "    try:\n",
    "        #按照月份来拟合训练模型\n",
    "        model = ARMA(df_month.Price,order=(param[0], param[1])).fit()#去遍历parameters_list里面的值 param[0]就是ps param[1]就是qs\n",
    "    except ValueError:\n",
    "        print('参数错误:', param)\n",
    "        continue\n",
    "    aic = model.aic#打擂法寻找最小的aic的模型\n",
    "    if aic < best_aic:\n",
    "        best_model = model\n",
    "        best_aic = aic\n",
    "        best_param = param\n",
    "    results.append([param, model.aic])\n",
    "# 输出最优模型\n",
    "print('最优模型: ', best_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step3，模型预测，可视化呈现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>116.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>132.628182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-28</th>\n",
       "      <td>131.887778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>126.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-30</th>\n",
       "      <td>118.426818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>2923.774700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>2962.063709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>3078.654681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>2927.513035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2990.415289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>352 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "1990-12-31   116.990000\n",
       "1991-01-31   132.628182\n",
       "1991-02-28   131.887778\n",
       "1991-03-31   126.011429\n",
       "1991-04-30   118.426818\n",
       "...                 ...\n",
       "2019-11-30  2923.774700\n",
       "2019-12-31  2962.063709\n",
       "2020-01-31  3078.654681\n",
       "2020-02-29  2927.513035\n",
       "2020-03-31  2990.415289\n",
       "\n",
       "[352 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置future_month，需要预测的时间date_list\n",
    "df_month2 = df_month[['Price']]\n",
    "df_month2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "352\n",
      "351\n",
      "2020-03-31 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-03-31 00:00:00', freq='M')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(df_month2))\n",
    "print(len(df_month2)-1)\n",
    "print(df_month2.index[len(df_month2)-1])\n",
    "last_month = pd.to_datetime(df_month2.index[len(df_month2)-1])\n",
    "last_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_list= [Timestamp('2020-04-30 00:00:00', freq='M'), Timestamp('2020-05-31 00:00:00', freq='M'), Timestamp('2020-06-30 00:00:00', freq='M')]\n"
     ]
    }
   ],
   "source": [
    "future_month = 3#未来三个月\n",
    "#print(last_month)\n",
    "date_list = []\n",
    "for i in range(future_month):\n",
    "    # 计算下个月有多少天\n",
    "    year = last_month.year#last.month就是上面2020-03-31\n",
    "    month = last_month.month\n",
    "    if month == 12:\n",
    "        month = 1\n",
    "        year = year+1\n",
    "    else:\n",
    "        month = month + 1\n",
    "    next_month_days = calendar.monthrange(year, month)[1]#查日历\n",
    "    #print(next_month_days)\n",
    "    last_month = last_month + timedelta(days=next_month_days)\n",
    "    date_list.append(last_month)\n",
    "print('date_list=', date_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>116.990000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>132.628182</td>\n",
       "      <td>138.837387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-28</th>\n",
       "      <td>131.887778</td>\n",
       "      <td>167.374518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>126.011429</td>\n",
       "      <td>161.454911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-30</th>\n",
       "      <td>118.426818</td>\n",
       "      <td>147.484033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>2927.513035</td>\n",
       "      <td>3097.990704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2990.415289</td>\n",
       "      <td>2847.906588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2982.564262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3001.069982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2953.355796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price     forecast\n",
       "1990-12-31   116.990000          NaN\n",
       "1991-01-31   132.628182   138.837387\n",
       "1991-02-28   131.887778   167.374518\n",
       "1991-03-31   126.011429   161.454911\n",
       "1991-04-30   118.426818   147.484033\n",
       "...                 ...          ...\n",
       "2020-02-29  2927.513035  3097.990704\n",
       "2020-03-31  2990.415289  2847.906588\n",
       "2020-04-30          NaN  2982.564262\n",
       "2020-05-31          NaN  3001.069982\n",
       "2020-06-30          NaN  2953.355796\n",
       "\n",
       "[355 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加未来要预测的3个月\n",
    "future = pd.DataFrame(index=date_list, columns= df_month.columns)\n",
    "df_month2 = pd.concat([df_month2, future])\n",
    "df_month2['forecast'] = best_model.predict(start=0, end=len(df_month2))#预测\n",
    "# 第一个元素不正确，设置为NaN\n",
    "df_month2['forecast'][0] = np.NaN\n",
    "df_month2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 沪市指数预测结果显示\n",
    "plt.figure(figsize=(30,7))\n",
    "df_month2.Price.plot(label='Real Stock Exchange Index')#实际的指数\n",
    "df_month2.forecast.plot(color='r', ls='--', label='Real Stock Exchange Index')#预测的指数\n",
    "plt.legend()\n",
    "plt.title('Shanghai Stock Exchange Index(month)')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('Exchange Index')\n",
    "plt.show()"
   ]
  }
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